hiepph

# https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix

from torch.autograd import Variable
from torchvision import transforms
from PIL import Image

from options.test_options import TestOptions

from models.models import create_model
import util.util as util

jFransham

Achieving warp speed with Rust

Contents:

• Number one optimization tip: don't
• Never optimize blindly
• Don't bother optimizing one-time costs
• Improve your algorithms
• CPU architecture primer
• Keep as much as possible in cache
• Keep as much as possible in registers

Neo23x0

Windows Registry Editor Version 5.00

[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\taskdl.exe]
"Debugger"="taskkill /F /IM "

[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\taskse.exe]
"Debugger"="taskkill /F /IM "

[HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Image File Execution Options\wannacry.exe]
"Debugger"="taskkill /F /IM "

dchest

SIV-like deterministic nonce-misuse resistant authenticated encryption construction with BLAKE2s and ChaCha20

Variant 1 (without associated data)

Seal (encryption and authentication)

• key - 32-byte secret key
• nonce - 8-byte nonce (all-zero by default)
• plaintext - data to encrypt and authenticate

max-mapper

urigoren

from keras.layers import Dense, Dropout, LSTM, Embedding
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
import pandas as pd
import numpy as np

input_file = 'input.csv'

def load_data(test_split = 0.2):
    print ('Loading data...')

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

justjanne

Server Price Breakdown: DigitalOcean, Amazon AWS LightSail, Vultr, Linode, OVH, Hetzner, Scaleway/Online.net:

Permalink: git.io/vps

$5/mo

Provider	Type	RAM	Cores	Storage	Transfer	Network	Price

brannondorsey

Image-to-Image Translation with Conditional Adversarial Networks

Notes from arXiv:1611.07004v1 [cs.CV] 21 Nov 2016

• Euclidean distance between predicted and ground truth pixels is not a good method of judging similarity because it yields blurry images.
• GANs learn a loss function rather than using an existing one.
• GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss.
• Conditional GANs (cGANs) learn a mapping from observed image x and random noise vector z to y: y = f(x, z)
• The generator G is trained to produce outputs that cannot be distinguished from "real" images by an adversarially trained discrimintor, D which is trained to do as well as possible at detecting the generator's "fakes".
• The discriminator D, learns to classify between real and synthesized pairs. The generator learns to fool the discriminator.
• Unlike an unconditional GAN, both th

summerwind

#define KBUILD_MODNAME "load_balancer"
#include <uapi/linux/bpf.h>
#include <linux/in.h>
#include <linux/if_ether.h>
#include <linux/if_packet.h>
#include <linux/if_vlan.h>
#include <linux/ip.h>
#include <linux/ipv6.h>

BPF_HASH(counter, uint32_t, long);